Overview

Dataset statistics

Number of variables22
Number of observations428
Missing cells3510
Missing cells (%)37.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory102.1 KiB
Average record size in memory244.3 B

Variable types

Numeric10
Unsupported8
Categorical4

Warnings

adm has constant value "20.0" Constant
operation_car has constant value "13.0" Constant
operation_date has a high cardinality: 279 distinct values High cardinality
rodvag is highly correlated with operation_car and 1 other fieldsHigh correlation
operation_car is highly correlated with rodvag and 1 other fieldsHigh correlation
adm is highly correlated with rodvag and 1 other fieldsHigh correlation
index_train has 428 (100.0%) missing values Missing
danger has 428 (100.0%) missing values Missing
loaded has 428 (100.0%) missing values Missing
operation_st_esr has 43 (10.0%) missing values Missing
operation_st_id has 43 (10.0%) missing values Missing
operation_train has 428 (100.0%) missing values Missing
rod_train has 428 (100.0%) missing values Missing
ssp_station_esr has 428 (100.0%) missing values Missing
ssp_station_id has 428 (100.0%) missing values Missing
weight_brutto has 428 (100.0%) missing values Missing
df_index has unique values Unique
car_number has unique values Unique
index_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
danger is an unsupported type, check if it needs cleaning or further analysis Unsupported
loaded is an unsupported type, check if it needs cleaning or further analysis Unsupported
operation_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
rod_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_esr is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight_brutto is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 143 (33.4%) zeros Zeros
sender has 103 (24.1%) zeros Zeros

Reproduction

Analysis started2021-04-16 09:37:47.561061
Analysis finished2021-04-16 09:38:08.492320
Duration20.93 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2246478.082
Minimum11081
Maximum4163938
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:08.698321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum11081
5-th percentile343299.8
Q11131633.75
median2591860
Q33292319.5
95-th percentile4014152.55
Maximum4163938
Range4152857
Interquartile range (IQR)2160685.75

Descriptive statistics

Standard deviation1248260.456
Coefficient of variation (CV)0.5556521854
Kurtosis-1.293025121
Mean2246478.082
Median Absolute Deviation (MAD)1096911.5
Skewness-0.1568934861
Sum961492619
Variance1.558154165 × 1012
MonotocityStrictly increasing
2021-04-16T15:38:08.930321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28661801
 
0.2%
29211491
 
0.2%
39743081
 
0.2%
27316971
 
0.2%
29170401
 
0.2%
38744791
 
0.2%
28463811
 
0.2%
30151991
 
0.2%
3713701
 
0.2%
40171381
 
0.2%
Other values (418)418
97.7%
ValueCountFrequency (%)
110811
0.2%
118471
0.2%
122761
0.2%
125161
0.2%
127961
0.2%
134791
0.2%
135921
0.2%
144641
0.2%
147001
0.2%
174941
0.2%
ValueCountFrequency (%)
41639381
0.2%
41476331
0.2%
41110041
0.2%
41106311
0.2%
41027571
0.2%
41025741
0.2%
40977721
0.2%
40831131
0.2%
40671521
0.2%
40557561
0.2%

index_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

length
Real number (ℝ≥0)

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.249415888
Minimum1
Maximum1.85
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:09.125320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.41
95-th percentile1.85
Maximum1.85
Range0.85
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.3496326408
Coefficient of variation (CV)0.2798368776
Kurtosis-0.977975317
Mean1.249415888
Median Absolute Deviation (MAD)0
Skewness0.9150809001
Sum534.75
Variance0.1222429835
MonotocityNot monotonic
2021-04-16T15:38:09.254322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1230
53.7%
1.8258
 
13.6%
1.0651
 
11.9%
1.4144
 
10.3%
1.8537
 
8.6%
1.838
 
1.9%
ValueCountFrequency (%)
1230
53.7%
1.0651
 
11.9%
1.4144
 
10.3%
1.8258
 
13.6%
1.838
 
1.9%
1.8537
 
8.6%
ValueCountFrequency (%)
1.8537
 
8.6%
1.838
 
1.9%
1.8258
 
13.6%
1.4144
 
10.3%
1.0651
 
11.9%
1230
53.7%

car_number
Real number (ℝ≥0)

UNIQUE

Distinct428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72474499.94
Minimum42162669
Maximum98096902
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:09.401356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum42162669
5-th percentile52765525.6
Q155713091.5
median61895837
Q394484090.25
95-th percentile98037953.85
Maximum98096902
Range55934233
Interquartile range (IQR)38770998.75

Descriptive statistics

Standard deviation19040806.94
Coefficient of variation (CV)0.2627242265
Kurtosis-1.767685953
Mean72474499.94
Median Absolute Deviation (MAD)8459214.5
Skewness0.32127804
Sum3.101908598 × 1010
Variance3.62552329 × 1014
MonotocityNot monotonic
2021-04-16T15:38:09.571356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551505931
 
0.2%
578086101
 
0.2%
534709441
 
0.2%
610997431
 
0.2%
587537571
 
0.2%
945394831
 
0.2%
527234181
 
0.2%
950862941
 
0.2%
557739071
 
0.2%
591889461
 
0.2%
Other values (418)418
97.7%
ValueCountFrequency (%)
421626691
0.2%
423017621
0.2%
426816351
0.2%
433489111
0.2%
521758661
0.2%
521881331
0.2%
522350661
0.2%
522665171
0.2%
523110811
0.2%
525033071
0.2%
ValueCountFrequency (%)
980969021
0.2%
980968941
0.2%
980968371
0.2%
980967791
0.2%
980962741
0.2%
980957301
0.2%
980957061
0.2%
980956491
0.2%
980870001
0.2%
980770761
0.2%

destination_esr
Real number (ℝ≥0)

Distinct37
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687292.8435
Minimum15805
Maximum988306
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:09.737319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum15805
5-th percentile181102
Q1547302
median817600
Q3817600
95-th percentile895703
Maximum988306
Range972501
Interquartile range (IQR)270298

Descriptive statistics

Standard deviation256585.1611
Coefficient of variation (CV)0.3733272702
Kurtosis0.1351110673
Mean687292.8435
Median Absolute Deviation (MAD)70003
Skewness-1.230644345
Sum294161337
Variance6.583594487 × 1010
MonotocityNot monotonic
2021-04-16T15:38:09.897324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
817600140
32.7%
88760345
 
10.5%
18110242
 
9.8%
54730224
 
5.6%
79730324
 
5.6%
64510022
 
5.1%
98830618
 
4.2%
23050618
 
4.2%
89570318
 
4.2%
89210311
 
2.6%
Other values (27)66
15.4%
ValueCountFrequency (%)
158051
 
0.2%
184091
 
0.2%
355082
 
0.5%
356011
 
0.2%
382053
 
0.7%
18110242
9.8%
1818082
 
0.5%
2053073
 
0.7%
2056081
 
0.2%
2205041
 
0.2%
ValueCountFrequency (%)
98830618
 
4.2%
9700011
 
0.2%
8958071
 
0.2%
89570318
 
4.2%
89210311
 
2.6%
88760345
 
10.5%
8502041
 
0.2%
8312031
 
0.2%
817600140
32.7%
7974077
 
1.6%

adm
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
20.0
428 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1712
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.0
2nd row20.0
3rd row20.0
4th row20.0
5th row20.0
ValueCountFrequency (%)
20.0428
100.0%
2021-04-16T15:38:10.204363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:38:10.301356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
20.0428
100.0%

Most occurring characters

ValueCountFrequency (%)
0856
50.0%
2428
25.0%
.428
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
75.0%
Other Punctuation428
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
0856
66.7%
2428
33.3%
ValueCountFrequency (%)
.428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1712
100.0%

Most frequent character per script

ValueCountFrequency (%)
0856
50.0%
2428
25.0%
.428
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1712
100.0%

Most frequent character per block

ValueCountFrequency (%)
0856
50.0%
2428
25.0%
.428
25.0%

danger
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

gruz
Real number (ℝ≥0)

Distinct13
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326531.9206
Minimum3009
Maximum693176
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:10.378354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile3009
Q13009
median351043
Q3693176
95-th percentile693176
Maximum693176
Range690167
Interquartile range (IQR)690167

Descriptive statistics

Standard deviation269724.0541
Coefficient of variation (CV)0.8260266061
Kurtosis-1.401900099
Mean326531.9206
Median Absolute Deviation (MAD)342133
Skewness0.1251166908
Sum139755662
Variance7.275106538 × 1010
MonotocityNot monotonic
2021-04-16T15:38:10.508360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3009136
31.8%
693176116
27.1%
39148357
13.3%
24160424
 
5.6%
32302424
 
5.6%
15106018
 
4.2%
41219615
 
3.5%
35130613
 
3.0%
35104312
 
2.8%
4523338
 
1.9%
Other values (3)5
 
1.2%
ValueCountFrequency (%)
3009136
31.8%
15106018
 
4.2%
24160424
 
5.6%
32302424
 
5.6%
3230583
 
0.7%
3510391
 
0.2%
35104312
 
2.8%
35130613
 
3.0%
39148357
13.3%
41219615
 
3.5%
ValueCountFrequency (%)
693176116
27.1%
4523338
 
1.9%
4520281
 
0.2%
41219615
 
3.5%
39148357
13.3%
35130613
 
3.0%
35104312
 
2.8%
3510391
 
0.2%
3230583
 
0.7%
32302424
 
5.6%

loaded
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

operation_car
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
13.0
428 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1712
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13.0
2nd row13.0
3rd row13.0
4th row13.0
5th row13.0
ValueCountFrequency (%)
13.0428
100.0%
2021-04-16T15:38:10.769362image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:38:10.855358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
13.0428
100.0%

Most occurring characters

ValueCountFrequency (%)
1428
25.0%
3428
25.0%
.428
25.0%
0428
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
75.0%
Other Punctuation428
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
1428
33.3%
3428
33.3%
0428
33.3%
ValueCountFrequency (%)
.428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1712
100.0%

Most frequent character per script

ValueCountFrequency (%)
1428
25.0%
3428
25.0%
.428
25.0%
0428
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1712
100.0%

Most frequent character per block

ValueCountFrequency (%)
1428
25.0%
3428
25.0%
.428
25.0%
0428
25.0%

operation_date
Categorical

HIGH CARDINALITY

Distinct279
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
2020-07-04 14:31:00
 
16
2020-07-02 02:15:00
 
15
2020-07-10 16:17:00
 
15
2020-07-22 10:28:00
 
13
2020-07-26 04:00:00
 
10
Other values (274)
359 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters8132
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)54.2%

Sample

1st row2020-07-16 15:42:00
2nd row2020-07-15 21:29:00
3rd row2020-07-15 21:15:00
4th row2020-07-16 13:31:00
5th row2020-07-16 17:10:00
ValueCountFrequency (%)
2020-07-04 14:31:0016
 
3.7%
2020-07-02 02:15:0015
 
3.5%
2020-07-10 16:17:0015
 
3.5%
2020-07-22 10:28:0013
 
3.0%
2020-07-26 04:00:0010
 
2.3%
2020-07-26 06:23:009
 
2.1%
2020-07-26 03:55:008
 
1.9%
2020-07-23 14:55:007
 
1.6%
2020-07-17 21:03:006
 
1.4%
2020-07-02 02:09:005
 
1.2%
Other values (269)324
75.7%
2021-04-16T15:38:11.147361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-07-1545
 
5.3%
2020-07-2635
 
4.1%
2020-07-2231
 
3.6%
2020-07-0226
 
3.0%
2020-07-3024
 
2.8%
2020-07-1724
 
2.8%
2020-07-1223
 
2.7%
2020-07-1620
 
2.3%
2020-07-2320
 
2.3%
2020-07-1919
 
2.2%
Other values (260)589
68.8%

Most occurring characters

ValueCountFrequency (%)
02595
31.9%
21262
15.5%
-856
 
10.5%
:856
 
10.5%
1581
 
7.1%
7565
 
6.9%
428
 
5.3%
3243
 
3.0%
5227
 
2.8%
6170
 
2.1%
Other values (3)349
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5992
73.7%
Dash Punctuation856
 
10.5%
Other Punctuation856
 
10.5%
Space Separator428
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
02595
43.3%
21262
21.1%
1581
 
9.7%
7565
 
9.4%
3243
 
4.1%
5227
 
3.8%
6170
 
2.8%
4161
 
2.7%
8104
 
1.7%
984
 
1.4%
ValueCountFrequency (%)
-856
100.0%
ValueCountFrequency (%)
428
100.0%
ValueCountFrequency (%)
:856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8132
100.0%

Most frequent character per script

ValueCountFrequency (%)
02595
31.9%
21262
15.5%
-856
 
10.5%
:856
 
10.5%
1581
 
7.1%
7565
 
6.9%
428
 
5.3%
3243
 
3.0%
5227
 
2.8%
6170
 
2.1%
Other values (3)349
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8132
100.0%

Most frequent character per block

ValueCountFrequency (%)
02595
31.9%
21262
15.5%
-856
 
10.5%
:856
 
10.5%
1581
 
7.1%
7565
 
6.9%
428
 
5.3%
3243
 
3.0%
5227
 
2.8%
6170
 
2.1%
Other values (3)349
 
4.3%

operation_st_esr
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)2.6%
Missing43
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean963201.5844
Minimum891909
Maximum986103
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:11.318330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum891909
5-th percentile891909
Q1980200
median980200
Q3980709
95-th percentile986103
Maximum986103
Range94194
Interquartile range (IQR)509

Descriptive statistics

Standard deviation34056.83059
Coefficient of variation (CV)0.03535794702
Kurtosis0.1977035368
Mean963201.5844
Median Absolute Deviation (MAD)509
Skewness-1.411196694
Sum370832610
Variance1159867710
MonotocityNot monotonic
2021-04-16T15:38:11.500322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
980200119
27.8%
89190960
14.0%
98060542
 
9.8%
98070940
 
9.3%
92720931
 
7.2%
98610326
 
6.1%
98510024
 
5.6%
98050122
 
5.1%
98470018
 
4.2%
9801073
 
0.7%
(Missing)43
 
10.0%
ValueCountFrequency (%)
89190960
14.0%
92720931
 
7.2%
9801073
 
0.7%
980200119
27.8%
98050122
 
5.1%
98060542
 
9.8%
98070940
 
9.3%
98470018
 
4.2%
98510024
 
5.6%
98610326
 
6.1%
ValueCountFrequency (%)
98610326
 
6.1%
98510024
 
5.6%
98470018
 
4.2%
98070940
 
9.3%
98060542
 
9.8%
98050122
 
5.1%
980200119
27.8%
9801073
 
0.7%
92720931
 
7.2%
89190960
14.0%

operation_st_id
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)2.6%
Missing43
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean2001809383
Minimum2000038848
Maximum2002030157
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:11.627319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2000038848
5-th percentile2000038848
Q12002024431
median2002025607
Q32002025651
95-th percentile2002030157
Maximum2002030157
Range1991309
Interquartile range (IQR)1220

Descriptive statistics

Standard deviation620367.2615
Coefficient of variation (CV)0.0003099032638
Kurtosis4.360967968
Mean2001809383
Median Absolute Deviation (MAD)778
Skewness-2.517587851
Sum7.706966126 × 1011
Variance3.848555392 × 1011
MonotocityNot monotonic
2021-04-16T15:38:11.834365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2002025607119
27.8%
200202443160
14.0%
200003884842
 
9.8%
200203013340
 
9.3%
200202482931
 
7.2%
200202566126
 
6.1%
200203015724
 
5.6%
200202560922
 
5.1%
200202565118
 
4.2%
20020256053
 
0.7%
(Missing)43
 
10.0%
ValueCountFrequency (%)
200003884842
 
9.8%
200202443160
14.0%
200202482931
 
7.2%
20020256053
 
0.7%
2002025607119
27.8%
200202560922
 
5.1%
200202565118
 
4.2%
200202566126
 
6.1%
200203013340
 
9.3%
200203015724
 
5.6%
ValueCountFrequency (%)
200203015724
 
5.6%
200203013340
 
9.3%
200202566126
 
6.1%
200202565118
 
4.2%
200202560922
 
5.1%
2002025607119
27.8%
20020256053
 
0.7%
200202482931
 
7.2%
200202443160
14.0%
200003884842
 
9.8%

operation_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

receiver
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9949365.22
Minimum0
Maximum95687583
Zeros143
Zeros (%)33.4%
Memory size3.5 KiB
2021-04-16T15:38:12.006320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median186424
Q316111351
95-th percentile48962205
Maximum95687583
Range95687583
Interquartile range (IQR)16111351

Descriptive statistics

Standard deviation21340115.67
Coefficient of variation (CV)2.14487208
Kurtosis8.702955192
Mean9949365.22
Median Absolute Deviation (MAD)186424
Skewness2.953674798
Sum4258328314
Variance4.55400537 × 1014
MonotocityNot monotonic
2021-04-16T15:38:12.139323image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0143
33.4%
186424140
32.7%
1611135142
 
9.8%
575349031
 
7.2%
2931705422
 
5.1%
2938183318
 
4.2%
9568758318
 
4.2%
489622053
 
0.7%
1868493
 
0.7%
677363483
 
0.7%
Other values (4)5
 
1.2%
ValueCountFrequency (%)
0143
33.4%
186424140
32.7%
1868031
 
0.2%
1868493
 
0.7%
575349031
 
7.2%
1611135142
 
9.8%
161462141
 
0.2%
2931705422
 
5.1%
2938183318
 
4.2%
304862991
 
0.2%
ValueCountFrequency (%)
9568758318
4.2%
677363483
 
0.7%
489622053
 
0.7%
417469122
 
0.5%
304862991
 
0.2%
2938183318
4.2%
2931705422
5.1%
161462141
 
0.2%
1611135142
9.8%
575349031
7.2%

rodvag
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
60.0
230 
96.0
192 
40.0
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1712
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row96.0
2nd row96.0
3rd row96.0
4th row96.0
5th row96.0
ValueCountFrequency (%)
60.0230
53.7%
96.0192
44.9%
40.06
 
1.4%
2021-04-16T15:38:12.409357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:38:12.509320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
60.0230
53.7%
96.0192
44.9%
40.06
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0664
38.8%
.428
25.0%
6422
24.6%
9192
 
11.2%
46
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1284
75.0%
Other Punctuation428
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
0664
51.7%
6422
32.9%
9192
 
15.0%
46
 
0.5%
ValueCountFrequency (%)
.428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1712
100.0%

Most frequent character per script

ValueCountFrequency (%)
0664
38.8%
.428
25.0%
6422
24.6%
9192
 
11.2%
46
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1712
100.0%

Most frequent character per block

ValueCountFrequency (%)
0664
38.8%
.428
25.0%
6422
24.6%
9192
 
11.2%
46
 
0.4%

rod_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

sender
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11102101.03
Minimum0
Maximum74981099
Zeros103
Zeros (%)24.1%
Memory size3.5 KiB
2021-04-16T15:38:12.599321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11125133
median1126016
Q310238843
95-th percentile73262854
Maximum74981099
Range74981099
Interquartile range (IQR)9113710

Descriptive statistics

Standard deviation20584041.56
Coefficient of variation (CV)1.854067217
Kurtosis4.203037918
Mean11102101.03
Median Absolute Deviation (MAD)1126016
Skewness2.304337655
Sum4751699242
Variance4.237027671 × 1014
MonotocityNot monotonic
2021-04-16T15:38:12.721326image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1126016118
27.6%
0103
24.1%
1023884364
15.0%
314462731
 
7.2%
7326285426
 
6.1%
112513324
 
5.6%
2703699524
 
5.6%
3117093118
 
4.2%
137377210
 
2.3%
749810999
 
2.1%
ValueCountFrequency (%)
0103
24.1%
112513324
 
5.6%
1126016118
27.6%
137377210
 
2.3%
314462731
 
7.2%
1023884364
15.0%
2703699524
 
5.6%
3117093118
 
4.2%
356903201
 
0.2%
7326285426
 
6.1%
ValueCountFrequency (%)
749810999
 
2.1%
7326285426
 
6.1%
356903201
 
0.2%
3117093118
 
4.2%
2703699524
 
5.6%
1023884364
15.0%
314462731
 
7.2%
137377210
 
2.3%
1126016118
27.6%
112513324
 
5.6%

ssp_station_esr
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

ssp_station_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

tare_weight
Real number (ℝ≥0)

Distinct41
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.1285047
Minimum194
Maximum261
Zeros0
Zeros (%)0.0%
Memory size3.5 KiB
2021-04-16T15:38:13.054355image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum194
5-th percentile208.35
Q1235
median240
Q3243
95-th percentile250
Maximum261
Range67
Interquartile range (IQR)8

Descriptive statistics

Standard deviation12.76796454
Coefficient of variation (CV)0.05384407312
Kurtosis3.094479163
Mean237.1285047
Median Absolute Deviation (MAD)5
Skewness-1.746332389
Sum101491
Variance163.0209186
MonotocityNot monotonic
2021-04-16T15:38:13.223319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25088
20.6%
24082
19.2%
23833
 
7.7%
23531
 
7.2%
24318
 
4.2%
24218
 
4.2%
23317
 
4.0%
23916
 
3.7%
24113
 
3.0%
19510
 
2.3%
Other values (31)102
23.8%
ValueCountFrequency (%)
1942
 
0.5%
19510
2.3%
1963
 
0.7%
2003
 
0.7%
2051
 
0.2%
2061
 
0.2%
2071
 
0.2%
2081
 
0.2%
2092
 
0.5%
21010
2.3%
ValueCountFrequency (%)
2611
 
0.2%
25088
20.6%
2482
 
0.5%
2471
 
0.2%
2463
 
0.7%
2455
 
1.2%
2446
 
1.4%
24318
 
4.2%
24218
 
4.2%
24113
 
3.0%

weight_brutto
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing428
Missing (%)100.0%
Memory size3.5 KiB

Interactions

2021-04-16T15:37:52.630062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:52.801061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:52.950069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.118061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.266068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.408065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.583064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.728066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:53.862062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.006062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.138062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.278059image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.409066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.559064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.685066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.829066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:54.961096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.098066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.230065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.368096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.508060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.661066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.812066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:55.958064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.124063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.272060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.420066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.568065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.700061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.832097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:56.981060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.124099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.260065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.416097image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.576060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.723066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:57.869060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:58.041061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:58.235061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:58.446069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:58.669068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:58.828061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:59.076064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:59.376062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:59.624063image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:37:59.896060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:00.045060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:00.178060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:00.413062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:00.616061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:00.780066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:01.353071image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:01.491065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:01.628066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:01.767099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:01.932065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.099062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.262060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.418060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.580062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.732096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:02.886060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.046098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.205061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.338060image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.468061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.611065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.749066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:03.898062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.030096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.186066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.332062image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.473065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.610103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.748061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:04.901065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.046064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.195099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.335066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.490064image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.633096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.781065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:05.915065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.052065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.197096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.351320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.499319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.764357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:06.916361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:38:07.081324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-16T15:38:13.409358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-16T15:38:13.773318image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-16T15:38:14.124324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-16T15:38:14.564319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-16T15:38:14.779356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-16T15:38:07.410360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-16T15:38:07.901358image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-16T15:38:08.132365image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-16T15:38:08.274361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
011081NaN1.0694496304525407.020.0NaN3009.0NaN13.02020-07-16 15:42:00980107.02.002026e+09NaN0.096.0NaN0.0NaNNaN195.0NaN
111847NaN1.839464449935601.020.0NaN3009.0NaN13.02020-07-15 21:29:00986103.02.002026e+09NaN48962205.096.0NaN73262854.0NaNNaN246.0NaN
212276NaN1.839469469235508.020.0NaN3009.0NaN13.02020-07-15 21:15:00986103.02.002026e+09NaN48962205.096.0NaN73262854.0NaNNaN248.0NaN
312516NaN1.4194778487892103.020.0NaN391483.0NaN13.02020-07-16 13:31:00891909.02.002024e+09NaN0.096.0NaN0.0NaNNaN246.0NaN
412796NaN1.4194798659525407.020.0NaN3009.0NaN13.02020-07-16 17:10:00980709.02.002030e+09NaN0.096.0NaN0.0NaNNaN240.0NaN
513479NaN1.0694292455525407.020.0NaN3009.0NaN13.02020-07-16 17:15:00980709.02.002030e+09NaN0.096.0NaN0.0NaNNaN240.0NaN
613592NaN1.0694309887887603.020.0NaN391483.0NaN13.02020-07-16 11:57:00891909.02.002024e+09NaN0.096.0NaN0.0NaNNaN220.0NaN
714464NaN1.0694430402887603.020.0NaN391483.0NaN13.02020-07-16 13:00:00891909.02.002024e+09NaN0.096.0NaN0.0NaNNaN207.0NaN
814700NaN1.0694451457887603.020.0NaN391483.0NaN13.02020-07-16 12:35:00891909.02.002024e+09NaN0.096.0NaN0.0NaNNaN195.0NaN
917494NaN1.4194849841887603.020.0NaN391483.0NaN13.02020-07-16 11:37:00891909.02.002024e+09NaN0.096.0NaN0.0NaNNaN240.0NaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
4184055756NaN1.0052235066797407.020.0NaN412196.0NaN13.02020-07-16 08:46:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN238.0NaN
4194067152NaN1.0642162669700308.020.0NaN351043.0NaN13.02020-07-16 14:28:00980200.02.002026e+09NaN0.040.0NaN1126016.0NaNNaN211.0NaN
4204083113NaN1.0654448576700308.020.0NaN351043.0NaN13.02020-07-16 14:25:00980200.02.002026e+09NaN0.040.0NaN1126016.0NaNNaN211.0NaN
4214097772NaN1.0053415550797407.020.0NaN412196.0NaN13.02020-07-16 06:26:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN233.0NaN
4224102574NaN1.0053475653797407.020.0NaN412196.0NaN13.02020-07-16 08:52:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN238.0NaN
4234102757NaN1.0053487385797407.020.0NaN412196.0NaN13.02020-07-16 08:32:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN243.0NaN
4244110631NaN1.0056876881797407.020.0NaN412196.0NaN13.02020-07-16 08:40:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN225.0NaN
4254111004NaN1.0056923824797407.020.0NaN412196.0NaN13.02020-07-16 08:24:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN236.0NaN
4264147633NaN1.0062178389797303.020.0NaN351306.0NaN13.02020-07-16 09:02:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN240.0NaN
4274163938NaN1.0060644879797407.020.0NaN412196.0NaN13.02020-07-16 06:52:00927209.02.002025e+09NaN5753490.060.0NaN3144627.0NaNNaN237.0NaN